Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/17373#discussion_r133082607
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifierSuite.scala
---
@@ -82,6 +83,49 @@ class MultilayerPerceptronClassifierSuite
}
}
+ test("strong dataset test") {
+ val layers = Array[Int](4, 5, 5, 2)
+
+ val strongDataset = Seq(
+ (Vectors.dense(1, 2, 3, 4), 0d, Vectors.dense(1d, 0d)),
+ (Vectors.dense(4, 3, 2, 1), 1d, Vectors.dense(0d, 1d)),
+ (Vectors.dense(1, 1, 1, 1), 0d, Vectors.dense(.5, .5)),
+ (Vectors.dense(1, 1, 1, 1), 1d, Vectors.dense(.5, .5))
+ ).toDF("features", "label", "expectedProbability")
+ val trainer = new MultilayerPerceptronClassifier()
+ .setLayers(layers)
+ .setBlockSize(1)
+ .setSeed(123L)
+ .setMaxIter(100)
+ .setSolver("l-bfgs")
+ val model = trainer.fit(strongDataset)
+ val result = model.transform(strongDataset)
+ model.setProbabilityCol("probability")
+ MLTestingUtils.checkCopyAndUids(trainer, model)
+ // result.select("probability").show(false)
+ result.select("probability", "expectedProbability").collect().foreach {
+ case Row(p: Vector, e: Vector) =>
+ assert(p ~== e absTol 1e-3)
+ }
+ }
+
+ test("test model probability") {
+ val layers = Array[Int](2, 5, 2)
+ val trainer = new MultilayerPerceptronClassifier()
+ .setLayers(layers)
+ .setBlockSize(1)
+ .setSeed(123L)
+ .setMaxIter(100)
+ .setSolver("l-bfgs")
+ val model = trainer.fit(dataset)
+ model.setProbabilityCol("probability")
+ val result = model.transform(dataset)
+ val features2prob = udf { features: Vector =>
model.mlpModel.predict(features) }
+ val cmpVec = udf { (v1: Vector, v2: Vector) => v1 ~== v2 relTol 1e-3 }
+ assert(result.select(cmpVec(features2prob(col("features")),
col("probability")))
--- End diff --
If this test fails, it will not give much info. How about collecting the
data and comparing on the driver?
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